The subject matter discussed in the background section should not be assumed to be prior art merely as a result of its mention in the background section. Similarly, a problem mentioned in the background section or associated with the subject matter of the background section should not be assumed to have been previously recognized in the prior art. The subject matter in the background section merely represents different approaches, which in and of themselves may also correspond to implementations of the claimed inventions.
In today's world, we are dealing with huge data volumes, popularly referred to as “Big Data”. Web applications that serve and manage millions of Internet users, such as FACEBOOK™, INSTAGRAM™, TWITTER™, banking websites, or even online retail shops, such as AMAZON.COM™ or EBAY™ are faced with the challenge of ingesting high volumes of data as fast as possible so that the end users can be provided with a real-time experience.
Another major contributor to Big Data is a concept and paradigm called “Internet of Things” (IoT). IoT is about a pervasive presence in the environment of a variety of things/objects that through wireless and wired connections are able to interact with each other and cooperate with other things/objects to create new applications/services. These applications/services are in areas likes smart cities (regions), smart car and mobility, smart home and assisted living, smart industries, public safety, energy and environmental protection, agriculture and tourism. Global data centers host thousands of enterprise companies, offering performance and security that enable organizations to serve and manage millions of Internet users of the IoT.
Currently, there is a need to collect and visualize metrics for disparate internet-connected devices. For example, global data centers that process the big data of an IoT ecosystem need to be monitored for reliability by their site reliability engineers. It has become imperative to increase the ability to customize the views needed by different groups of users for monitoring operational status of computing devices and systems. Production support teams for global data centers monitor the infrastructure of global data centers 24 hours a day, seven days a week—monitoring metrics for thousands of hardware systems, including servers and IoT devices. In turn, thousands of visualizations need to be designed and generated to display those metrics, with different teams across an enterprise developing and deploying visualizations.
Production support teams often use a real-time graphing system for monitoring and graphing the performance of computer systems, to monitor metrics and graph system performance. Historically, those who develop new dashboard visualizations have faced the challenges of developing and testing the new dashboard before deploying it in a production development.
When a designer exports a newly developed dashboard to a non-production server for testing, no production data is available in the development environment. That is, the developer is “flying blind” in the sense that they miss the production metrics—they do not know whether the dashboard will function as desired when loaded into production and displaying production data. Also, when a new dashboard is submitted to the production server environment, the new dashboard overwrites the existing dashboard on the server. Errors—often caused by the inability to test the design with production data prior to deployment—can result in down time during which no dashboard is available to production support teams who need to monitor the metrics, so no system covered by the affected dashboard(s) can be monitored.
Therefore, an opportunity arises to provide systems and methods for developing and testing dashboards with production data in non-production environments. Efficient monitoring of global data centers, increased system reliability and uptime percentages, and improved user experience may result.
The disclosed technology relates to making production data available for testing in a non-production environment.